Quran-MD: A Fine-Grained Multilingual Multimodal Dataset of the Quran
This dataset addresses a gap in computational resources for Quranic studies, enabling research in NLP, speech processing, and digital Islamic applications, though it is incremental as it builds on existing data collection efforts.
The authors tackled the lack of a comprehensive multimodal dataset for the Quran by creating Quran-MD, which integrates textual, linguistic, and audio data at verse and word levels, including audio from 32 reciters and supporting applications like NLP and speech recognition.
We present Quran MD, a comprehensive multimodal dataset of the Quran that integrates textual, linguistic, and audio dimensions at the verse and word levels. For each verse (ayah), the dataset provides its original Arabic text, English translation, and phonetic transliteration. To capture the rich oral tradition of Quranic recitation, we include verse-level audio from 32 distinct reciters, reflecting diverse recitation styles and dialectical nuances. At the word level, each token is paired with its corresponding Arabic script, English translation, transliteration, and an aligned audio recording, allowing fine-grained analysis of pronunciation, phonology, and semantic context. This dataset supports various applications, including natural language processing, speech recognition, text-to-speech synthesis, linguistic analysis, and digital Islamic studies. Bridging text and audio modalities across multiple reciters, this dataset provides a unique resource to advance computational approaches to Quranic recitation and study. Beyond enabling tasks such as ASR, tajweed detection, and Quranic TTS, it lays the foundation for multimodal embeddings, semantic retrieval, style transfer, and personalized tutoring systems that can support both research and community applications. The dataset is available at https://huggingface.co/datasets/Buraaq/quran-audio-text-dataset